Evidence map›Paper›PMID 21092328›Full record

ArticleMicrobial cell factories2010

The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies.

Kyung Yun Lee, Jong Myoung Park, Tae Yong Kim, Hongseok Yun, Sang Yup Lee

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Article in Microbial cell factories, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

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29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

  1. Review
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  7. Zymomonas mobilis ZM4 Utilizes an NADPJournal of bacteriology · 2022
    Article
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  14. Improvement of Acetaldehyde Production inFrontiers in microbiology · 2019
    Article
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  18. Review
  19. Article
  20. Applications of genome-scale metabolic network model in metabolic engineering.Journal of industrial microbiology & biotechnology · 2015
    Review
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kyung Yun LeeMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea.
Jong Myoung Park
Tae Yong Kim
Hongseok Yun
Sang Yup Lee

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundZymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions.

resultsThe genome-scale metabolic model of Z. mobilis ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of Z. mobilis were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of Z. mobilis as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum in silico yields of ethanol for Z. mobilis and Escherichia coli were compared and analyzed. Furthermore, the substrate utilization range of Z. mobilis was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow Z. mobilis to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of Z. mobilis.

conclusionThe genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of Z. mobilis under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of Z. mobilis metabolism and consequently designing metabolic engineering strategies for various biotechnological applications.

Indexed as

Genome, BacterialEscherichia coliEthanolMetabolic Networks and PathwaysModels, BiologicalSuccinic AcidZymomonasEthanolSuccinic Acid

Identifiers

PMID21092328
PMCPMC3004842

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.